(1) Field of Invention
The present invention relates to a system for rapid object detection and, more particularly, to a system for rapid object detection that combines structural information from image segmentation with bio-inspired attentional mechanisms.
(2) Description of Related Art
Object detection and recognition has been studied in the computer vision domain for many years. In the object detection domain, sliding windows and saliency (“object-ness”) are traditionally used for finding candidates of objects. The sliding window based approach typically requires a large amount of computation, since there are many windows to test. Many works exhaustively search over the entire image region at different locations, scales, and orientations. This can significantly slow down the object detection algorithm. This computational problem is often more serious for high-dimensional problems and real-time applications. Recently, there have been efforts in improving the searching approach given the initial candidates (also referred to as hypotheses). Efficient sliding-windows search (ESS) (see the List of Cited Literature References, Literature Reference No. 3) and particle swarm optimization (PSO) (see Literature Reference No. 4) are shown to improve the speed of finding objects from those initial candidates. Nevertheless, ESS and PSO approaches focus on improving the searching stage, but do not improve the initial detection of candidates. Moreover, they usually require an iterative processing and can be significantly slowed down for images containing many objects.
On the other hand, saliency based approaches typically focus attention on image areas with high saliency. Such approaches are usually more computationally efficient, but suffer from the difficulty of defining a generic saliency metric (see Literature Reference No. 1).
A recent publication by Russakovsky and Ng (see Literature Reference No. 2) used a graph-based image segmentation approach to generate initial candidates. Their technique required a training stage to find the optimal set of parameters for their five-step sequential processing. Moreover, experiments were performed with small size images in less complex scenarios.
Each of the prior methods described above exhibit limitations that make them incomplete. Thus, a continuing need exists for an object detection system which does not have a time consuming training stage, can be used for wide-view aerial images, and uses fast image segmentation with attention based features for simple, efficient, and accurate object detection.